Importance of hyperparameter tuning

Benchmark study, using 73 datasets from OpenML-CC18, on the importance of hyperparameter tuning: which parameters are important to tune and which might be set to a default value instead?
For each dataset, the following experiments were ran.
* Experiment 1: gather performance data to determine good default parameters
* Flow 8351: 1000 random configurations of sklearn RandomForestClassifier with a training time limit of 3 hours.
* Flow 8353: 1000 random configurations of sklearn SVC with a training time limit of 3 hours.
For each of the 59 datasets for which more than 900 performance data points are retrieved in the previous experiment, the following experiments were ran:
* Experiment 2: RandomizedGridSearchCV(cv = 5, n\_iter = 100) with one hyperparameter fixed
* Flow 8365: RandomForestClassifier(n\_estimators = 300), 10 random search seeds, 5 hyperparameters (_bootstrap_, _criterion_, _max\_features_, _min\_samples\_leaf_, _min\_samples\_split) and a control group with no parameters fixed.
* Flow 8399: SVC(kernel = 'rbf'), 10 random search seeds, 4 hyperparameters (_gamma_, _C_, _tol_, _shrinking_) and a control group with no parameters fixed.
For more information, see: https://github.com/hildeweerts/hyperimp
We thank Microsoft Azure for providing the computational resources for this study.